FEM-Validated Real-Time Modelling of Soft Tactile Deformatio
Overview
Source roundup from published reports. Claims below are attributed to their publishers, not independently verified. arXiv Robotics — research abstracts: FEM-Validated Real-Time Modelling of Soft Tactile Deformation. This research presents a data-efficient framework that combines limited real-world validation, high-fidelity FEM, and graph neural networks (GNNs) for accurate real-time prediction of two-dimensional tactile deformation …
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Reported quantity · contact configurations: 82 other · Basis not reported
“ited real-world validation, high-fidelity FEM, and graph neural networks (GNNs) for accurate real-time prediction of two-dimensional tactile deformation in a planar soft tactile structure. The key idea is to decouple validation from training: 82 camera-tracked contact configurations are used only to tune and validate an ANSYS FEM model, which then generates 768 synthetic contact scenarios for GNN training. We evaluat”
Exact source · revision 1Source owner not reported
Reported quantity · synthetic contact scenarios: 768 other · Basis not reported
“ited real-world validation, high-fidelity FEM, and graph neural networks (GNNs) for accurate real-time prediction of two-dimensional tactile deformation in a planar soft tactile structure. The key idea is to decouple validation from training: 82 camera-tracked contact configurations are used only to tune and validate an ANSYS FEM model, which then generates 768 synthetic contact scenarios for GNN training. We evaluat”
Exact source · revision 1Source owner not reported
Reported quantity · mean absolute error: 0.32 other · Basis not reported
“e both forward prediction from marker deformation to contact localisation and inverse inference from contact configuration to marker deformation. The validated FEM achieves sub-0.5 mm error in more than 90% of validation cases, while the learned GNN surrogate reaches a mean absolute error of 0.32 mm with millisecond-scale inference, compared with minute-scale FEM simulation. Models trained entirely on synthetic FEM d”
Exact source · revision 1Source owner not reported
Reported quantity · error: 0.5 other · Basis not reported
“e both forward prediction from marker deformation to contact localisation and inverse inference from contact configuration to marker deformation. The validated FEM achieves sub-0.5 mm error in more than 90% of validation cases, while the learned GNN surrogate reaches a mean absolute error of 0.32 mm with millisecond-scale inference, compared with minute-scale FEM simulation. Models trained entirely on synthetic FEM d”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsFEM-Validated Real-Time Modelling of Soft Tactile Deformation
This research presents a data-efficient framework that combines limited real-world validation, high-fidelity FEM, and graph neural networks (GNNs) for accurate real-time prediction of two-dimensional tactile deformation in a planar soft tactile structure. The validated FEM model achieves sub-0.5 mm error in over 90% of validation cases, while the learned GNN surrogate reaches a mean absolute error of 0.32 mm with millisecond-scale inference, compared to minute-scale FEM simulation. The model demonstrates strong sim-to-real transfer and is integrated into a real-time visualization pipeline for virtual prototyping and design exploration.
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